The present invention relates generally to the analysis of skeletal trauma, and more particularly to the automated detection, quantification, and intervention planning of skeletal trauma using deep learning.
The exposure of the worldwide population to major trauma is consequential. To improve the survival chances of a patient, it is important to quickly and reliably identify injuries with high mortality risk and to accelerate treatment of those identified injuries. In the current clinical practice, a clinician follows diagnostic imaging pathways guidelines to determine anatomies to be scanned and the imaging modalities of the scan. For example, according to diagnostic imaging pathways guidelines, a clinician may analyze computed tomography (CT) images in bone window and in 3D multiplanar reformats to detect bone fractures. To do so, the clinicians identify general patterns on the skeleton to identify fractures and assess the fractures using general standards. Some bones, such as the pelvis or the ribs, are analyzed in more detail and using more specific standards. However, the sheer volume of the whole body CT images and the time pressure to diagnose leads to misdiagnosis or delayed diagnosis of bone fractures, which may result in poor patient outcomes such as physical disability or death.